An Intelligent Cross Domain Enterprise Framework for AI Cloud Blockchain IoT and Predictive Cyber Defense
DOI:
https://doi.org/10.15662/IJARCST.2025.08050.38Keywords:
Artificial Intelligence, Enterprise Computing, Cloud Computing, Blockchain, Internet of Things, IoT, Predictive Cyber Defense, Cybersecurity, Machine Learning, Digital Transformation, Enterprise Architecture, Smart Systems, Cloud Security, Intelligent Automation, Data Analytics, Distributed Ledger Technology, Edge Computing, Sustainable Digital InnovationAbstract
The accelerating pace of digital transformation has encouraged enterprises to adopt integrated technologies that improve operational efficiency, cybersecurity, scalability, and intelligent decision-making. Artificial Intelligence (AI), cloud computing, blockchain, the Internet of Things (IoT), and predictive cyber defense have emerged as critical enablers of next-generation enterprise ecosystems capable of supporting sustainable innovation across multiple business domains. AI provides intelligent automation, predictive analytics, and autonomous decision support, while cloud computing offers scalable infrastructure and flexible resource management for enterprise applications. Blockchain technology establishes decentralized trust through secure, transparent, and immutable transaction management, enabling trusted collaboration among distributed stakeholders. IoT extends enterprise intelligence by connecting physical devices, sensors, and industrial systems to generate real-time operational data for continuous monitoring and optimization. Predictive cyber defense enhances organizational resilience by employing machine learning, behavioral analytics, and threat intelligence to identify vulnerabilities and prevent sophisticated cyberattacks before they impact business operations. This paper proposes an intelligent cross-domain enterprise framework that integrates AI, cloud computing, blockchain, IoT, and predictive cyber defense into a unified architecture for secure and sustainable digital transformation. The proposed framework facilitates intelligent resource management, trusted data sharing, automated threat detection, and real-time decision support while improving business agility, operational transparency, and regulatory compliance. The study demonstrates how technological convergence enables enterprises to achieve resilient, adaptive, and future-ready digital ecosystems capable of addressing evolving business, technological, and cybersecurity challenges.References
1. Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson.
2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
3. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
4. Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence. Academy of Management Annals, 14(2), 627–660.
5. Stallings, W. (2018). Network Security Essentials (6th ed.). Pearson.
6. Aguru, A., & Erukala, S. (2024). OTI-IoT: A blockchain-based operational threat intelligence framework for multi-vector DDoS attacks. ACM Transactions on Internet Technology, 24(3).
7. Nazir, A., He, J., Zhu, N., et al. (2024). Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration. Journal of King Saud University – Computer and Information Sciences, 36, 101939.
8. Fernandez-Carames, T. M., & Fraga-Lamas, P. (2024). Tides of blockchain in IoT cybersecurity. Sensors, 24(10), 3111.
9. Potdar, A., Gottipalli, D., Ashirova, A., Kodela, V., Donkina, S., & Begaliev, A. (2025, July). MFO-AIChain: An Intelligent Optimization and Blockchain-Backed Architecture for Resilient and Real-Time Healthcare IoT Communication. In 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3) (pp. 1-6). IEEE.
10. Sonawane, S. (2025). Analytical Study of Real-Time Policy Enforcement in Global Sales Quoting for Compliance-Aware CPQ Automation. World Research of Business Administration Journal, 5(3).
11. Alex Mathew. (2023). Threat defense through cyber fusion. International Journal of Computer Science and Mobile Computing, 12(1), 24–27. https://doi.org/10.47760/ijcsmc.2022.v12i01.003
12. Gurram, S. K. (2025). Revolutionizing financial infrastructure: the convergence of blockchain and cloud in next-generation payment networks. Journal of Computer Science and Technology Studies, 7(4), 607-618.
13. Mohammed, S. (2024). Strategic cloud cost optimization and FinOps governance for global enterprises. International Journal of Research and Applied Innovations (IJRAI), 7(6), 12004–12008.
14. Joyce, S. (2024). Automated enterprise system reliability: Integrating AI-driven monitoring with cloud-based SAP deployment pipelines. International Journal of Research and Applied Innovations, 7(2), 10474-10482.
15. Anumula, S. K. (2025). Next-gen supply chains: A product lifecycle management–based approach to resilient and sustainable operations. International Journal of Managing Value and Supply Chains (IJMVSC), 16.
16. Polamreddy, V. R. (2025). Incremental Change Processing and Financial Data Integrity in Enterprise Cloud Adoption Programs. International Journal of Research and Applied Innovations, 8(1), 11749-11761.
17. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105-5111.
18. Kotla, M. R. T. (2024). Optimizing enterprise integration pipelines using cloud-native data engineering and middleware solutions. International Journal of Research Publications in Engineering, Technology and Management, 7(5), 11311–11314.
19. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
20. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
21. Gopinathan, V. R. (2024). Cyber-Resilient Digital Banking Analytics Using AI-Driven Federated Machine Learning on AWS. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8419-8426.
22. Velishala, S. (2025). AI-based decision support systems for healthcare DevOps: Improving reliability and decision-making in software development. Journal of Advanced Research in Engineering and Technology, 2(1).
23. Anand, L. (2024). AI-Powered Cloud Cybersecurity Architecture for Risk Prediction and Threat Mitigation in Healthcare and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(Special Issue 1), 5-12.
24. Anbazhagan, K. (2024). Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures. International Journal of Technology, Management and Humanities, 10(03), 65-74.
25. Pothuri, M. K. (2025). The role of data governance in achieving compliance and trust in healthcare and fintech. IJAIDR–Journal of Advances in Developmental Research, 16(2).
26. Navandar, P. (2024). Quantum safe public key infrastructure: Hybrid classical PQC certificate chains and migration framework for enterprise TLS. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8153–8160. https://doi.org/10.15662/IJEETR.2024.0604014
27. Alti, B. (2025). AI-driven continuous security validation for enterprise Linux systems using configuration-as-code. Fuel Cells Bulletin, 2025, 459-471.
28. Veershetty, G. (2022). Digital modernization of gas utility operations: Architecture, scaled-agile delivery, and assurance. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7796.
29. Kandula, S. T. R. (2025, July). Comparison and Performance Assessment of Intelligent ML Models for Forecasting Cardiovascular Disease Risks in Healthcare. In 2025 International Conference on Sensors and Related Networks (SENNET) Special Focus on Digital Healthcare (64220) (pp. 1-6). IEEE.
30. Mathew, A. (2025). Secure and Scalable AI-Integrated Cloud Infrastructure for HIPAA-Compliant Healthcare Financial Operations. International Journal of Future Innovative Science and Technology (IJFIST), 8(4), 15296.
31. Shewale, V. (2022). Third-Party and Supply Chain Risk in Oil & Gas. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9596.
32. Juvvadi, R. R. (2019). Smart contracts in supply chain finance: Automating accounts payable and the three-way match. Journal of Information Systems Engineering and Management, 4(1), 1–12.
33. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
34. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471
35. Islam Khan, B. U., et al. (2024). Integrating AI and blockchain for enhanced data security in IoT-driven smart cities. Processes, 12(9), 1825.
36. Suhail, S., et al. (2024). TRIPLE: A blockchain-based digital twin framework for cyber–physical systems security. Journal of Industrial Information Integration, 42, 100706.
